Less is More: A Streamlined Graph-Based Fashion Outfit Recommendation without Multimodal Dependency
Author:
Affiliation:
1. Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
2. Hansung University, School of Computer Engineering, Seoul, Republic of Korea
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3605098.3636168
Reference8 articles.
1. POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
2. Zeyu Cui Zekun Li Shu Wu Xiao-Yu Zhang and Liang Wang. 2019. Dressing as a whole: Outfit compatibility learning based on node-wise graph neural networks. In The world wide web conference. 307--317.
3. Yashar Deldjoo Fatemeh Nazary Arnau Ramisa Julian McAuley Giovanni Pellegrini Alejandro Bellogín and T. D. Noia. 2022. A Review of Modern Fashion Recommender Systems. ArXiv abs/2202.02757 (2022).
4. Learning Fashion Compatibility with Bidirectional LSTMs
5. Paras Jain Zhanghao Wu Matthew A. Wright Azalia Mirhoseini Joseph E. Gonzalez and Ion Stoica. 2021. Representing Long-Range Context for Graph Neural Networks with Global Attention. In Advances in Neural Information Processing Systems A. Beygelzimer Y. Dauphin P. Liang and J. Wortman Vaughan (Eds.). https://openreview.net/forum?id=nYz2_BbZnYk
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